https://github.com/vislearn/FrEIA
Framework for Easily Invertible Architectures
https://github.com/vislearn/FrEIA
Last synced: about 1 year ago
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Framework for Easily Invertible Architectures
- Host: GitHub
- URL: https://github.com/vislearn/FrEIA
- Owner: vislearn
- License: mit
- Created: 2018-09-07T10:45:17.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-07-03T11:58:12.000Z (almost 2 years ago)
- Last Synced: 2024-10-30T10:55:15.864Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 11.3 MB
- Stars: 766
- Watchers: 20
- Forks: 112
- Open Issues: 38
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
- awesome-normalizing-flows - FrEIA
README
|Logo|
.. image:: https://github.com/vislearn/FrEIA/workflows/CI/badge.svg
:alt: Build Status
This is the **Fr**\ amework for **E**\ asily **I**\ nvertible **A**\ rchitectures (**FrEIA**).
* Construct Invertible Neural Networks (INNs) from simple invertible building blocks.
* Quickly construct complex invertible computation graphs and INN topologies.
* Forward and inverse computation guaranteed to work automatically.
* Most common invertible transforms and operations are provided.
* Easily add your own invertible transforms.
.. contents:: Table of contents
:backlinks: top
:local:
Papers
--------------
Our following papers use FrEIA, with links to code given below.
**"Generative Classifiers as a Basis for Trustworthy Image Classification" (CVPR 2021)**
* Paper: https://arxiv.org/abs/2007.15036
* Code: https://github.com/RayDeeA/ibinn_imagenet
**"Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (Neurips 2020)**
* Paper: `arxiv.org/abs/2001.06448 `_
* Code: `github.com/vislearn/IB-INN `_
**"Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (ICLR 2020)**
* Paper: `arxiv.org/abs/2001.04872 `_
* Code: `github.com/vislearn/GIN `_
**"Guided Image Generation with Conditional Invertible Neural Networks" (2019)**
* Paper: `arxiv.org/abs/1907.02392 `_
* Supplement: `drive.google.com/file/d/1_OoiIGhLeVJGaZFeBt0OWOq8ZCtiI7li `_
* Code: `github.com/vislearn/conditional_INNs `_
**"Analyzing inverse problems with invertible neural networks." (ICLR 2019)**
* Paper: `arxiv.org/abs/1808.04730 `_
* Code: `github.com/vislearn/analyzing_inverse_problems `_
Installation
--------------
FrEIA has the following dependencies:
+---------------------------+-------------------------------+
| **Package** | **Version** |
+---------------------------+-------------------------------+
| Python | >= 3.7 |
+---------------------------+-------------------------------+
| Pytorch | >= 1.0.0 |
+---------------------------+-------------------------------+
| Numpy | >= 1.15.0 |
+---------------------------+-------------------------------+
| Scipy | >= 1.5 |
+---------------------------+-------------------------------+
Through pip
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code:: sh
pip install FrEIA
Manually
^^^^^^^^^^^^^^^^^^^^^^^^^^^
For development:
.. code:: sh
# first clone the repository
git clone https://github.com/vislearn/FrEIA.git
cd FrEIA
# install the dependencies
pip install -r requirements.txt
# install in development mode, so that changes don't require a reinstall
python setup.py develop
Documentation
-----------------
The full manual can be found at
https://vislearn.github.io/FrEIA
including
* `Quickstart guide `_
* `Tutorial `_
* `Examples `_
* `API documentation `_
How to cite this repository
-------------------------------
If you used this repository in your work, please cite it as below:
.. code-block::
@software{freia,
author = {Ardizzone, Lynton and Bungert, Till and Draxler, Felix and Köthe, Ullrich and Kruse, Jakob and Schmier, Robert and Sorrenson, Peter},
title = {{Framework for Easily Invertible Architectures (FrEIA)}},
year = {2018-2022},
url = {https://github.com/vislearn/FrEIA}
}
.. |Logo| image:: docs/freia_logo_invertible.svg